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Adaptive noise removal from biomedical signals using warped polynomials

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Abstract
This paper presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals, The filter fits warped polynomials to large segments of such signals, This can be interpreted as low-pass filtering with a time-varying cutoff frequency, In optimal operation, the filter's cut-off frequency equals the local signal bandwidth, However, the paper also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth, The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals, In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction, However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction, This is the way nonquasiperiodical signals are filtered, The paper presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from EGG, respiratory and blood pressure signals, and base line restoration of electro-encephalograms (EEG's).
Keywords
ENHANCEMENT, QRS DETECTION

Citation

Please use this url to cite or link to this publication:

Chicago
Philips, Wilfried. 1996. “Adaptive Noise Removal from Biomedical Signals Using Warped Polynomials.” Ieee Transactions on Biomedical Engineering 43 (5): 480–492.
APA
Philips, Wilfried. (1996). Adaptive noise removal from biomedical signals using warped polynomials. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 43(5), 480–492.
Vancouver
1.
Philips W. Adaptive noise removal from biomedical signals using warped polynomials. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1996;43(5):480–92.
MLA
Philips, Wilfried. “Adaptive Noise Removal from Biomedical Signals Using Warped Polynomials.” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 43.5 (1996): 480–492. Print.
@article{187865,
  abstract     = {This paper presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals, The filter fits warped polynomials to large segments of such signals, This can be interpreted as low-pass filtering with a time-varying cutoff frequency, In optimal operation, the filter's cut-off frequency equals the local signal bandwidth, However, the paper also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth, The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals, In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction, However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction, This is the way nonquasiperiodical signals are filtered, The paper presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from EGG, respiratory and blood pressure signals, and base line restoration of electro-encephalograms (EEG's).},
  author       = {Philips, Wilfried},
  issn         = {0018-9294},
  journal      = {IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING},
  keyword      = {ENHANCEMENT,QRS DETECTION},
  language     = {eng},
  number       = {5},
  pages        = {480--492},
  title        = {Adaptive noise removal from biomedical signals using warped polynomials},
  url          = {http://dx.doi.org/10.1109/10.488796},
  volume       = {43},
  year         = {1996},
}

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